부산대학교 도서관

Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce ${\rm PROFIL}_{R}$, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). ${\rm PROFIL}_{R}$ endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows that ${\rm PROFIL}_{R}$ is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.